AI Product Engineer - Agentic AI Platforms (Financial Services) · Capgemini

Fri, 24 Apr 2026 14:15:34 GMT
Engineering

Capgemini · Charlotte · Competitive salary and performance-based bonuses · hybrid

Capgemini is at the forefront of Generative AI innovation, helping Financial Services clients industrialize GenAI and Agentic AI platforms at enterprise scale.

We are seeking an experienced and innovative AI Product Engineer – Agentic Platforms to join our Financial Services Artificial Intelligence & Business Lines (FS-ABL) practice. This role is ideal for a consulting technologist with deep expertise in modern GenAI tooling, agentic system design, and enterprise SDLC, who can partner directly with clients to envision, design, develop, and deploy Agentic AI platforms in regulated environments.

In this role, you will work at the intersection of client advisory, AI product engineering, and delivery execution, helping banks, insurers, and capital markets firms transition from GenAI pilots to production-grade, governed, multi-agent systems. You will apply leading GenAI frameworks and LLM platforms , including Anthropic, OpenAI, LangChain, LangGraph, DSPy, and vector databases,while operating across the full Agentic SDLC.

P&C Insurance knowledge and experience is a significant plus. Additionally, familiarity with core insurance platforms like Guidewire, DuckCreek or Majesco will be extremely helpful to succeed in this role.

We are looking for candidates across all levels of experience and expertise - junior through senior level AI Product Engineers.

Responsibilities:

Client Advisory & Product Vision

Partner directly with Financial Services clients to identify, prioritize, and shape Agentic AI use cases across customer operations, underwriting, claims, risk, compliance, finance, and technology.

Lead client workshops to define agent personas, responsibilities, autonomy boundaries, human-in-the-loop checkpoints, and escalation logic.

Translate evolving business needs into agentic product backlogs, roadmaps, and MVP definitions.

Support executive conversations around GenAI platform strategy, operating models, vendor selection, and scale-out approaches.

Agentic Platform & Architecture Design

Design and implement multi-agent architectures using modern GenAI tooling, including:

Planner, executor, reviewer/critic, and supervisor agents

Tool-calling and function-calling agents

Memory-enabled agents (conversation, semantic, episodic, and structured memory)

Leverage LangChain and LangGraph for agent orchestration, workflows, and control flow.

Apply DSPy and declarative prompt optimization techniques for repeatability, performance tuning, and regression control.

Design agent interaction patterns such as hierarchical agents, collaborating agents, and event-driven agent workflows.

Define standardized agent contracts, interfaces, and schemas to enable reuse and scale.

Agentic SDLC & Engineering Delivery

Own delivery across the full Software Development Lifecycle (SDLC), extending it into a formal Agentic SDLC, including:

Agent design specifications and behavior contracts

Prompt, policy, and tool versioning

Simulation environments and offline evaluation

Automated testing of agent flows and guardrails

Controlled rollout, telemetry-driven optimization, and continuous learning

Build production-grade AI services primarily using Python, integrating:

LLM providers such as Anthropic (Claude), OpenAI, and open-source models

Retrieval-Augmented Generation (RAG) using vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate)

Implement CI/CD pipelines for agent code, prompts, and policies.

Integrate GenAI agents with client systems via APIs, workflow engines, event streams, and data platforms.

Observability, Evaluation & Optimization

Implement agent observability including tracing, decision logging, tool usage, and failure analysis.

Apply evaluation frameworks for hallucination detection, consistency checks, and fitness scoring.

Design feedback loops incorporating human-in-the-loop review and reinforcement.

Monitor cost, latency, throughput, and behavioral drift across deployed agents.

Governance, Risk & Financial Services Compliance

Design Agentic AI platforms aligned with Financial Services regulatory expectations, including:

Auditability and traceability of agent decisions

Model and prompt explainability

Data privacy and security controls

Resilience and fail-safe mechanisms

Embed guardrails and policies addressing hallucination risk, bias, unauthorized actions, and escalation failures.

Produce documentation supporting risk, compliance, internal audit, and regulator engagement.

Team Leadership & Firm Contribution

Provide technical leadership and mentorship to consulting delivery teams.

Contribute to internal GenAI accelerators, agent frameworks, and reusable assets.

Support RFPs, proposals, and client solution designs with credible GenAI and agentic architectures.

Participate in thought leadership on Agentic SDLC, GenAI engineering, and responsible autonomy.

Benefits

This position comes with competitive compensation and benefits package:

Competitive salary and performance-based bonuses

Comprehensive benefits package

Career development and training opportunities

Flexible work arrangements (remote and/or office-based)

Dynamic and inclusive work culture within a globally known group

Private Health Insurance

Retirement Benefits

Paid Time Off

Training & Development

Industry: Technology

Category: Engineering

Salary Range: Competitive salary and performance-based bonuses

Salary Min: 95000

Salary Max: 150000

Salary Currency: USD

Salary Period: year

Required Skills:

Generative AI

Agentic AI

Agent-based systems

Multi-agent architectures

LLM platforms

Vector databases

Python

CI/CD pipelines

APIs

Workflow engines

Event streams

Data platforms

Observability

Evaluation frameworks

Human-in-the-loop review

Reinforcement learning

Governance

Risk management

Financial Services compliance

Team leadership

Mentorship

Thought leadership

Preferred Skills:

Cloud computing

Containerization

DevOps

Machine learning

Natural language processing

Recommendation systems

deployed agents